遺傳算法的選擇機制

Question

我已經構建了一個遺傳算法，但我覺得我的代碼的選擇/變異部分出了問題。 這是我正在談論的代碼的一部分：

#include "stdafx.h"
#include <iostream>
#include <vector>
#include <random>
#include <string>
#include <iomanip>
#include <math.h>

// The random number generator I am using.
std::random_device rd;
std::mt19937 rng(rd());

for (int k = 1; k < population_size; k++)                       // Loop until new population is filled up. K = 1 because first individual has the best genes from last generation.
{
// Calculate total fitness.

double totalfitness = 0;

for (int i = 0; i < population_size; i++)
{
    totalfitness += individuals[i].fitness;
}

// Calculate  relative fitness.

for (int i = 0; i < population_size; i++)
{
    individuals[i].probability = individuals[i].fitness / totalfitness;
}

std::uniform_real_distribution<double> dist2(0.0, 1.0);     // Initiate random number generator to generate a double between 0 and 1.

double rndNumber = dist2(rng);                              // Generate first double
double rndNumber2 = dist2(rng);                             // Generate second double
double offset = 0.0;                                        // Set offset (starting point from which it'll add up probabilities) at 0.
int father = 0;                                             // father is the individual that is picked, initialize at 0.
int mother = 0;

// Pick first parent. Once picked, set the fitness for that individual at 0 so that it can not be picked again.

for (int i = 0; i < population_size; i++)
{
    offset += individuals[i].probability;
    if (rndNumber < offset)
    {
        father = i;
        individuals[i].fitness = 0.0;
        break;
    }
}

offset = 0.0;       // Reset offset to zero because we'll start again for the second parent.
totalfitness = 0;   // Recalculate total fitness using only the remaining individuals and reset total fitness to 0

// Here we recalculate total fitness using only the fitness of the individuals remaining.

for (int i = 0; i < population_size; i++)
{
    totalfitness += individuals[i].fitness;
}

// Then we recalculate probability for the individuals based on the new totalfitness.

for (int i = 0; i < population_size; i++)
{
    individuals[i].probability = individuals[i].fitness / totalfitness;
}

// Then we give back the old fitness to the father/mother

individuals[father].fitness = 1 / (individuals[father].evaluation*individuals[father].evaluation);

// Then pick parent 2.

for (int i = 0; i < population_size; i++)
{
    offset += individuals[i].probability;
    if (rndNumber2 < offset)
    {
        mother = i;
        break;
    }
}

// Having picked father and mother, now the idea is to run a random number generator between 0 and 1 for each gene.
// So if:   father  {5, 8, 9, 3}
//          mother  {1, 5, 2, 6)
//          rndnum  {0, 0, 1, 1}
// then     child   {5, 8, 2, 6}

std::uniform_int_distribution<int> gene_selection(0, 1);        // Initiate random number generator to generate an integer between 0 and 1.

for (int i = 0; i < number_of_variables; i++)
{
    int gene1 = gene_selection(rng);
    if (gene1 == 0)
    {
        new_individuals[k].chromosomes[0].push_back(individuals[father].chromosomes[0].at(i));
    }
    else if (gene1 == 1)
    {
        new_individuals[k].chromosomes[0].push_back(individuals[mother].chromosomes[0].at(i));
    }
}

for (int j = 0; j < number_of_variables; j++)
{
    for (int l = 0; l < 32; l++)
    {
        std::uniform_int_distribution<int> mutation(0, 50);
        int mutation_outcome = mutation(rng);
        if (mutation_outcome == 1)
        {
            new_individuals[k].chromosomes[0].at(j) ^= (1 << l);
            if (new_individuals[k].chromosomes[0].at(j) == 0)
            {
                int new_var = uni(rng);
                new_individuals[k].chromosomes[0].at(j) = new_var;
            }
        }
    }
}
}

// When all new individuals have values, give individuals values of new_individuals and start next round of evaluation.

for (int i = 0; i < population_size; i++)
{
individuals[i] = new_individuals[i];
}

我的代碼似乎工作正常。 我似乎無法弄清楚它為何會逐漸惡化。 它的前幾代似乎經常找到新的，更好的解決方案。 幾代之后，它停止尋找新的最佳解決方案。

這當然可能是因為沒有更好的解決方案，但我也同時在excel中進行計算，並且個人通常可以通過將其中一條“染色體”增加1來獲得更好的適應性，這通常是一個1位的變化，因為我通常用10000個人運行這個代碼，你會說程序必然會創建一個具有這種變異的個體。

我現在已經使用調試器多次遍歷我的代碼，在每一步顯示值等等，但我似乎無法找出它出錯的地方所以我想我會在這里發布我的代碼看看有沒有人能發現我搞砸了。

只是為了記錄，該算法只是一個公式求解器。 所以我可以例如輸入a = 1，b = 6，target = 50，a * gene1 + b * gene2並且它（理論上）指定更高的適應度，個體越接近獲得該結果。

另外，如果我不得不猜測我搞砸了，我會說它在代碼的變異部分：

for (int j = 0; j < number_of_variables; j++)
{
    for (int l = 0; l < 32; l++)
    {
        std::uniform_int_distribution<int> mutation(0, 50);
        int mutation_outcome = mutation(rng);
        if (mutation_outcome == 1)
        {
            new_individuals[k].chromosomes[0].at(j) ^= (1 << l);
            if (new_individuals[k].chromosomes[0].at(j) == 0)
            {
                int new_var = uni(rng);
                new_individuals[k].chromosomes[0].at(j) = new_var;
            }
        }
    }
}

我這樣說只是因為這是我最不理解的部分，我可以想象我在那里犯了一個“看不見的”錯誤。

無論如何，任何幫助將不勝感激。

Answer 1

嗯，這只是讓您的代碼更好，更高效的一種方法。 您正在使用std::uniform_int_distribution而沒有播種，並且幾乎連續5次調用，也許這就是為什么our random number is not really random after all 。

to get things better一個簡單方法是seeding the random engine with time ，從而在長期運行中給予更好的隨機數創建（10000個人，不知何故大！）。

這里有一個更好解釋的鏈接 ，一個簡單的代碼片段如下：

#include <iostream>
#include <random> 

std::default_random_engine generator((unsigned int)time(0));
int random(int n) {
  std::uniform_int_distribution<int> distribution(0, n);
  return distribution(generator);
}
int main() {
        for(int i = 0; i < 15; ++i)
                std::cout << random(5) << " " << random(5)<< std::endl;
        return 0;
}

希望有所幫助！ 干杯，